When not to use a graph database

If your access pattern is mostly "insert a row, fetch it by id, update a field" at high throughput, a relational or key-value store is a better fit. Graphs pay for their expressive traversals with more expensive inserts and storage per record; you only want to pay that when you're going to traverse. A shopping cart, a session store, an event counter — reach for Postgres, Redis or DynamoDB.

"Sum revenue by region for the last quarter," "average latency per endpoint per day" — scans and aggregations over big columns are what columnar warehouses are built for. A graph can answer them, but a column store like BigQuery, ClickHouse or Snowflake will do it faster and cheaper. Use the graph for the connected questions and hand the number-crunching to the warehouse.

If you're moving a firehose of events from A to B with transformations in between, that's a log and a stream processor — Kafka, Flink, a queue. A graph might be the destination where those events land and connect, but it isn't the transport. Model the graph as the queryable store the pipeline feeds, not the pipeline itself.

If the job is "find the most semantically similar passages to this text," that's a vector store. CognoDB doesn't do vector search, and we won't pretend otherwise — pair it with a dedicated vector index. The two work well together (see graph RAG vs vector RAG), but for pure similarity, the graph isn't the tool.

Ask what your hardest query is really about. If it's "get this record," "sum this column," or "find text like this," you probably don't need a graph. If it's "what is this connected to, how, and how far," — recommendations, fraud rings, dependencies, lineage, identity, agent memory — then the relationships are the value, and that's exactly when a graph earns its place.